Setup

!pip install -q spotipy

import pandas as pd
import numpy as np
import json
import re 
import sys
import itertools

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt


import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
from spotipy.oauth2 import SpotifyOAuth
import spotipy.util as util

import warnings
warnings.filterwarnings("ignore")

%matplotlib inline

Data Loading

!pip install -q -U kaggle
!pip install --upgrade --force-reinstall --no-deps kaggle
!mkdir ~/.kaggle
!cp /content/drive/MyDrive/kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download -d yamaerenay/spotify-dataset-19212020-160k-tracks

!unzip /content/spotify-dataset-19212020-160k-tracks.zip

spotify_df = pd.read_csv('tracks.csv')

spotify_df.head()

Observations:

This data is at a song level
Many numerical values that I'll be able to use to compare movies (liveness, tempo, valence, etc)
Release date will useful but I'll need to create a OHE variable for release date in 5 year increments
Similar to 2, I'll need to create OHE variables for the popularity. I'll also use 5 year increments here
There is nothing here related to the genre of the song which will be useful. This data alone won't help us find relavent content since this is a content based recommendation system.

data_w_genre = pd.read_csv('artists.csv')
data_w_genre.head()

Observations:

This data is at an artist level
There are similar continuous variables as our initial dataset but I won't use this. I'll just use the values int he previous dataset.
The genres are going to be really useful here and I'll need to use it moving forward. Now, the genre column appears to be in a list format but my past experience tells me that it's likely not. Let's investigate this further.

data_w_genre.dtypes

mode                  int64
genres               object
acousticness        float64
danceability        float64
duration_ms         float64
energy              float64
instrumentalness    float64
liveness            float64
loudness            float64
speechiness         float64
tempo               float64
valence             float64
popularity          float64
key                   int64
dtype: object

Voila, now we have the genre column in a format we can actually use. If you go down, you'll see how we use it.

Now, if you recall, this data is at a artist level and the previous dataset is at a song level. So what here's what we need to do:

Explode artists column in the previous so each artist within a song will have their own row
Merge data_w_genre to the exploded dataset in Step 1 so that the previous dataset no is enriched with genre dataset

Before I go further, let's complete these two steps.

Step 1. Similar to before, we will need to extract the artists from the string list.

spotify_df['artists_upd_v1'] = spotify_df['artists'].apply(lambda x: re.findall(r"'([^']*)'", x))

spotify_df['artists'].values[0]

"['Carl Woitschach']"

spotify_df['artists_upd_v1'].values[0][0]

'Carl Woitschach'

This looks good but did this work for every artist string format. Let's double check

spotify_df[spotify_df['artists_upd_v1'].apply(lambda x: not x)].head(5)

So, it looks like it didn't catch all of them and you can quickly see that it's because artists with an apostrophe in their title and the fact that they are enclosed in a full quotes. I'll write another regex to handle this and then combine the two

spotify_df['artists_upd_v2'] = spotify_df['artists'].apply(lambda x: re.findall('\"(.*?)\"',x))
spotify_df['artists_upd'] = np.where(spotify_df['artists_upd_v1'].apply(lambda x: not x), spotify_df['artists_upd_v2'], spotify_df['artists_upd_v1'] )

spotify_df['artists_song'] = spotify_df.apply(lambda row: row['artists_upd'][0]+row['name'],axis = 1)

spotify_df.sort_values(['artists_song','release_date'], ascending = False, inplace = True)

spotify_df[spotify_df['name']=='Adore You']

spotify_df.drop_duplicates('artists_song',inplace = True)

spotify_df[spotify_df['name']=='Adore You']

Now I can explode this column and merge as I planned to in Step 2

artists_exploded = spotify_df[['artists_upd','id']].explode('artists_upd')

artists_exploded_enriched = artists_exploded.merge(data_w_genre, how = 'left', left_on = 'artists_upd',right_on = 'artists')
artists_exploded_enriched_nonnull = artists_exploded_enriched[~artists_exploded_enriched.genres_upd.isnull()]

artists_exploded_enriched_nonnull[artists_exploded_enriched_nonnull['id'] =='6KuQTIu1KoTTkLXKrwlLPV']

Alright we're almost their, now we need to:

Group by on the song id and essentially create lists lists
Consilidate these lists and output the unique values

artists_genres_consolidated = artists_exploded_enriched_nonnull.groupby('id')['genres_upd'].apply(list).reset_index()

artists_genres_consolidated['consolidates_genre_lists'] = artists_genres_consolidated['genres_upd'].apply(lambda x: list(set(list(itertools.chain.from_iterable(x)))))

artists_genres_consolidated.head()

spotify_df = spotify_df.merge(artists_genres_consolidated[['id','consolidates_genre_lists']], on = 'id',how = 'left')

Feature Engineering

Normalize float variables
OHE Year and Popularity Variables
Create TF-IDF features off of artist genres

spotify_df.tail()

spotify_df['year'] = spotify_df['release_date'].apply(lambda x: x.split('-')[0])

float_cols = spotify_df.dtypes[spotify_df.dtypes == 'float64'].index.values

ohe_cols = 'popularity'

spotify_df['popularity'].describe()

count    155469.000000
mean         31.306987
std          21.422973
min           0.000000
25%          12.000000
50%          33.000000
75%          48.000000
max          97.000000
Name: popularity, dtype: float64

spotify_df['popularity_red'] = spotify_df['popularity'].apply(lambda x: int(x/5))

spotify_df['consolidates_genre_lists'] = spotify_df['consolidates_genre_lists'].apply(lambda d: d if isinstance(d, list) else [])

spotify_df.head()

#this gets passed later on
def ohe_prep(df, column, new_name): 
    """ 
    Create One Hot Encoded features of a specific column

    Parameters: 
        df (pandas dataframe): Spotify Dataframe
        column (str): Column to be processed
        new_name (str): new column name to be used
        
    Returns: 
        tf_df: One hot encoded features 
    """
    
    tf_df = pd.get_dummies(df[column])
    feature_names = tf_df.columns
    tf_df.columns = [new_name + "|" + str(i) for i in feature_names]
    tf_df.reset_index(drop = True, inplace = True)    
    return tf_df

from IPython.display import Image
Image("/Users/thakm004/Documents/Spotify/tfidf_4.png")

def create_feature_set(df, float_cols):
    """ 
    Process spotify df to create a final set of features that will be used to generate recommendations

    Parameters: 
        df (pandas dataframe): Spotify Dataframe
        float_cols (list(str)): List of float columns that will be scaled 
        
    Returns: 
        final: final set of features 
    """
    
    #tfidf genre lists
    tfidf = TfidfVectorizer()
    tfidf_matrix =  tfidf.fit_transform(df['consolidates_genre_lists'].apply(lambda x: " ".join(x)))
    genre_df = pd.DataFrame(tfidf_matrix.toarray())
    genre_df.columns = ['genre' + "|" + i for i in tfidf.get_feature_names()]
    genre_df.reset_index(drop = True, inplace=True)

    #explicity_ohe = ohe_prep(df, 'explicit','exp')    
    year_ohe = ohe_prep(df, 'year','year') * 0.5
    popularity_ohe = ohe_prep(df, 'popularity_red','pop') * 0.15

    #scale float columns
    floats = df[float_cols].reset_index(drop = True)
    scaler = MinMaxScaler()
    floats_scaled = pd.DataFrame(scaler.fit_transform(floats), columns = floats.columns) * 0.2

    #concanenate all features
    final = pd.concat([genre_df, floats_scaled, popularity_ohe, year_ohe], axis = 1)
     
    #add song id
    final['id']=df['id'].values
    
    return final

complete_feature_set = create_feature_set(spotify_df, float_cols=float_cols)#.mean(axis = 0)

complete_feature_set.head()

Connect to Spotify API

Useful links:

client_id = 'id'
client_secret= 'secret'

scope = 'user-library-read'

if len(sys.argv) > 1:
    username = sys.argv[1]
else:
    print("Usage: %s username" % (sys.argv[0],))
    sys.exit()

auth_manager = SpotifyClientCredentials(client_id=client_id, client_secret=client_secret)
sp = spotipy.Spotify(auth_manager=auth_manager)

token = util.prompt_for_user_token(scope, client_id= client_id, client_secret=client_secret, redirect_uri='http://localhost:8881/')

sp = spotipy.Spotify(auth=token)

#images aren't going to be used until I start building a UI
id_name = {}
list_photo = {}
for i in sp.current_user_playlists()['items']:

    id_name[i['name']] = i['uri'].split(':')[2]
    list_photo[i['uri'].split(':')[2]] = i['images'][0]['url']

id_name

{'My Peloton Music by madsthaks': '2ERUubOm9fCqSehYVio8bY',
 'chill': '0VjrxXF0S8wdkr8MLUaoFO',
 'EDM': '49q1SNpthDWA9D5ECbas6J',
 'My Shazam Tracks': '4nSIFeOEHqb0APbPVBvOy2',
 'kanye': '6lMQskYSwbK1RJUXeZYmv4',
 'marathon': '15RqI2qiCq5jD2KU0mD1I5',
 'Starred': '4mVCQMqnVVmZYIDnKjAImj',
 'Underground Hits': '37i9dQZF1DX30HHrCAl4ZG',
 'DJ Top 50 🎧 club music, remix, bootleg, mashup': '6jVUGg95c9A37iGOj2GMBb',
 'Running Electro': '3JDbVHBjEpM4PEuNwBlMRG',
 'Quentin Tarantino’s Django Unchained Original Motion Picture Soundtrack': '47ANSoONFl9iZlgErkYwmj',
 'Epic': '2Np7ZFr3jwITwuCvtxe5m6',
 'Alternative R&B': '3HG3Uca5gTcmZIEmyJl8NB',
 'Epic Classical': '37i9dQZF1DX9G9wwzwWL2k',
 'Chill': '3Atmxx4GS7g9U1IhAin1VL',
 'Electro House +': '2izYujPRo6mMFCGVuJMlFW',
 '3LAU': '41qGXzlHqXZwABw6D9l7LA',
 'Club/House/Electro/Trance': '62mXsdRriKgYjliP82CSti'}

def create_necessary_outputs(playlist_name,id_dic, df):
    """ 
    Pull songs from a specific playlist.

    Parameters: 
        playlist_name (str): name of the playlist you'd like to pull from the spotify API
        id_dic (dic): dictionary that maps playlist_name to playlist_id
        df (pandas dataframe): spotify datafram
        
    Returns: 
        playlist: all songs in the playlist THAT ARE AVAILABLE IN THE KAGGLE DATASET
    """
    
    #generate playlist dataframe
    playlist = pd.DataFrame()
    playlist_name = playlist_name

    for ix, i in enumerate(sp.playlist(id_dic[playlist_name])['tracks']['items']):
        #print(i['track']['artists'][0]['name'])
        playlist.loc[ix, 'artist'] = i['track']['artists'][0]['name']
        playlist.loc[ix, 'name'] = i['track']['name']
        playlist.loc[ix, 'id'] = i['track']['id'] # ['uri'].split(':')[2]
        playlist.loc[ix, 'url'] = i['track']['album']['images'][1]['url']
        playlist.loc[ix, 'date_added'] = i['added_at']

    playlist['date_added'] = pd.to_datetime(playlist['date_added'])  
    
    playlist = playlist[playlist['id'].isin(df['id'].values)].sort_values('date_added',ascending = False)
    
    return playlist

id_name

{'My Peloton Music by madsthaks': '2ERUubOm9fCqSehYVio8bY',
 'chill': '0VjrxXF0S8wdkr8MLUaoFO',
 'EDM': '49q1SNpthDWA9D5ECbas6J',
 'My Shazam Tracks': '4nSIFeOEHqb0APbPVBvOy2',
 'kanye': '6lMQskYSwbK1RJUXeZYmv4',
 'marathon': '15RqI2qiCq5jD2KU0mD1I5',
 'Starred': '4mVCQMqnVVmZYIDnKjAImj',
 'Underground Hits': '37i9dQZF1DX30HHrCAl4ZG',
 'DJ Top 50 🎧 club music, remix, bootleg, mashup': '6jVUGg95c9A37iGOj2GMBb',
 'Running Electro': '3JDbVHBjEpM4PEuNwBlMRG',
 'Quentin Tarantino’s Django Unchained Original Motion Picture Soundtrack': '47ANSoONFl9iZlgErkYwmj',
 'Epic': '2Np7ZFr3jwITwuCvtxe5m6',
 'Alternative R&B': '3HG3Uca5gTcmZIEmyJl8NB',
 'Epic Classical': '37i9dQZF1DX9G9wwzwWL2k',
 'Chill': '3Atmxx4GS7g9U1IhAin1VL',
 'Electro House +': '2izYujPRo6mMFCGVuJMlFW',
 '3LAU': '41qGXzlHqXZwABw6D9l7LA',
 'Club/House/Electro/Trance': '62mXsdRriKgYjliP82CSti'}

playlist_EDM = create_necessary_outputs('EDM', id_name,spotify_df)
#playlist_chill = create_necessary_outputs('chill',id_name, spotify_df)
#playlist_classical = create_necessary_outputs('Epic Classical',id_name, spotify_df)

from skimage import io
import matplotlib.pyplot as plt

def visualize_songs(df):
    """ 
    Visualize cover art of the songs in the inputted dataframe

    Parameters: 
        df (pandas dataframe): Playlist Dataframe
    """
    
    temp = df['url'].values
    plt.figure(figsize=(15,int(0.625 * len(temp))))
    columns = 5
    
    for i, url in enumerate(temp):
        plt.subplot(len(temp) / columns + 1, columns, i + 1)

        image = io.imread(url)
        plt.imshow(image)
        plt.xticks(color = 'w', fontsize = 0.1)
        plt.yticks(color = 'w', fontsize = 0.1)
        plt.xlabel(df['name'].values[i], fontsize = 12)
        plt.tight_layout(h_pad=0.4, w_pad=0)
        plt.subplots_adjust(wspace=None, hspace=None)

    plt.show()

playlist_EDM

visualize_songs(playlist_EDM)

Create Playlist Vector

from IPython.display import Image
Image("/Users/thakm004/Documents/Spotify/summarization_2.png")

def generate_playlist_feature(complete_feature_set, playlist_df, weight_factor):
    """ 
    Summarize a user's playlist into a single vector

    Parameters: 
        complete_feature_set (pandas dataframe): Dataframe which includes all of the features for the spotify songs
        playlist_df (pandas dataframe): playlist dataframe
        weight_factor (float): float value that represents the recency bias. The larger the recency bias, the most priority recent songs get. Value should be close to 1. 
        
    Returns: 
        playlist_feature_set_weighted_final (pandas series): single feature that summarizes the playlist
        complete_feature_set_nonplaylist (pandas dataframe): 
    """
    
    complete_feature_set_playlist = complete_feature_set[complete_feature_set['id'].isin(playlist_df['id'].values)]#.drop('id', axis = 1).mean(axis =0)
    complete_feature_set_playlist = complete_feature_set_playlist.merge(playlist_df[['id','date_added']], on = 'id', how = 'inner')
    complete_feature_set_nonplaylist = complete_feature_set[~complete_feature_set['id'].isin(playlist_df['id'].values)]#.drop('id', axis = 1)
    
    playlist_feature_set = complete_feature_set_playlist.sort_values('date_added',ascending=False)

    most_recent_date = playlist_feature_set.iloc[0,-1]
    
    for ix, row in playlist_feature_set.iterrows():
        playlist_feature_set.loc[ix,'months_from_recent'] = int((most_recent_date.to_pydatetime() - row.iloc[-1].to_pydatetime()).days / 30)
        
    playlist_feature_set['weight'] = playlist_feature_set['months_from_recent'].apply(lambda x: weight_factor ** (-x))
    
    playlist_feature_set_weighted = playlist_feature_set.copy()
    #print(playlist_feature_set_weighted.iloc[:,:-4].columns)
    playlist_feature_set_weighted.update(playlist_feature_set_weighted.iloc[:,:-4].mul(playlist_feature_set_weighted.weight,0))
    playlist_feature_set_weighted_final = playlist_feature_set_weighted.iloc[:, :-4]
    #playlist_feature_set_weighted_final['id'] = playlist_feature_set['id']
    
    return playlist_feature_set_weighted_final.sum(axis = 0), complete_feature_set_nonplaylist

complete_feature_set_playlist_vector_EDM, complete_feature_set_nonplaylist_EDM = generate_playlist_feature(complete_feature_set, playlist_EDM, 1.09)
#complete_feature_set_playlist_vector_chill, complete_feature_set_nonplaylist_chill = generate_playlist_feature(complete_feature_set, playlist_chill, 1.09)

complete_feature_set_playlist_vector_EDM.shape

(2758,)

Generate Recommendations

from IPython.display import Image
Image("/Users/thakm004/Documents/Spotify/cosine_sim_2.png")

def generate_playlist_recos(df, features, nonplaylist_features):
    """ 
    Pull songs from a specific playlist.

    Parameters: 
        df (pandas dataframe): spotify dataframe
        features (pandas series): summarized playlist feature
        nonplaylist_features (pandas dataframe): feature set of songs that are not in the selected playlist
        
    Returns: 
        non_playlist_df_top_40: Top 40 recommendations for that playlist
    """
    
    non_playlist_df = df[df['id'].isin(nonplaylist_features['id'].values)]
    non_playlist_df['sim'] = cosine_similarity(nonplaylist_features.drop('id', axis = 1).values, features.values.reshape(1, -1))[:,0]
    non_playlist_df_top_40 = non_playlist_df.sort_values('sim',ascending = False).head(40)
    non_playlist_df_top_40['url'] = non_playlist_df_top_40['id'].apply(lambda x: sp.track(x)['album']['images'][1]['url'])
    
    return non_playlist_df_top_40

edm_top40 = generate_playlist_recos(spotify_df, complete_feature_set_playlist_vector_EDM, complete_feature_set_nonplaylist_EDM)

from IPython.display import Image
Image("/Users/thakm004/Documents/Spotify/spotify_results.png")

edm_top40

visualize_songs(edm_top40)

chill_top40 = generate_playlist_recos(spotify_df, complete_feature_set_playlist_vector_chill, complete_feature_set_nonplaylist_chill)

	id	name	popularity	duration_ms	artists	id_artists	release_date	danceability	energy	key	loudness	mode	speechiness	acousticness	instrumentalness	liveness	valence	tempo	time_signature
0	35iwgR4jXetI318WEWsa1Q	Carve	6	126903	['Uli']	['45tIt06XoI0Iio4LBEVpls']	1922-02-22	0.645	0.4450	0	-13.338	1	0.4510	0.674	0.7440	0.151	0.127	104.851	3
1	021ht4sdgPcrDgSk7JTbKY	Capítulo 2.16 - Banquero Anarquista	0	98200	['Fernando Pessoa']	['14jtPCOoNZwquk5wd9DxrY']	1922-06-01	0.695	0.2630	0	-22.136	1	0.9570	0.797	0.0000	0.148	0.655	102.009	1
2	07A5yehtSnoedViJAZkNnc	Vivo para Quererte - Remasterizado	0	181640	['Ignacio Corsini']	['5LiOoJbxVSAMkBS2fUm3X2']	1922-03-21	0.434	0.1770	1	-21.180	1	0.0512	0.994	0.0218	0.212	0.457	130.418	5
3	08FmqUhxtyLTn6pAh6bk45	El Prisionero - Remasterizado	0	176907	['Ignacio Corsini']	['5LiOoJbxVSAMkBS2fUm3X2']	1922-03-21	0.321	0.0946	7	-27.961	1	0.0504	0.995	0.9180	0.104	0.397	169.980	3
4	08y9GfoqCWfOGsKdwojr5e	Lady of the Evening	0	163080	['Dick Haymes']	['3BiJGZsyX9sJchTqcSA7Su']	1922	0.402	0.1580	3	-16.900	0	0.0390	0.989	0.1300	0.311	0.196	103.220	4

	acousticness	artists	danceability	duration_ms	energy	id	instrumentalness	key	liveness	loudness	mode	name	popularity	release_date	speechiness	tempo	valence	year	artists_upd_v1
127	0.995	["Sam Manning's and His Cole Jazz Orchestra"]	0.664	173333	0.283	42WDMm9hX0xCFkkKpt6NOY	0.87400	8	0.109	-18.301	0	Bungo	0	1930-01-01	0.0807	99.506	0.688	1930	[]
180	0.984	["Scarlet D'Carpio"]	0.400	142443	0.190	4Gcc2YB0AAlzPLQhosdyAw	0.90000	0	0.182	-12.062	1	Chililin Uth'aja	0	1930	0.0492	81.290	0.402	1930	[]
1244	0.506	["Original Broadway Cast Of 'Flahooley"]	0.519	35227	0.475	1Qt9zpHUfVqMNr25EU9IFL	0.07100	7	0.103	-9.553	0	Prologue	0	1951-01-01	0.1070	105.639	0.615	1951	[]
1478	0.809	["Cal Tjader's Modern Mambo Quintet"]	0.795	238200	0.386	5VeW5QJDW906P5knRgJWzt	0.87400	1	0.106	-14.984	1	Dearly Beloved	2	1954-09-11	0.0570	119.800	0.807	1954	[]
1944	0.804	["Screamin' Jay Hawkins"]	0.574	142893	0.401	6MC85zBk1dQqnywRDdzy7h	0.00002	2	0.546	-11.185	1	I Love Paris	14	1958	0.0533	89.848	0.587	1958	[]

	acousticness	artists	danceability	duration_ms	energy	id	instrumentalness	key	liveness	loudness	mode	name	popularity	release_date	speechiness	tempo	valence	year	artists_upd_v1	artists_upd_v2	artists_upd	artists_song
97046	0.1110	['Miley Cyrus']	0.583	278747	0.655	5AnCLGg35ziFOloEnXK4uu	0.000004	0	0.113	-5.407	1	Adore You	70	2013-10-04	0.0315	119.759	0.201	2013	[Miley Cyrus]	[]	[Miley Cyrus]	Miley CyrusAdore You
87868	0.0237	['Harry Styles']	0.676	207133	0.771	3jjujdWJ72nww5eGnfs2E7	0.000007	8	0.102	-3.675	1	Adore You	88	2019-12-13	0.0483	99.048	0.569	2019	[Harry Styles]	[]	[Harry Styles]	Harry StylesAdore You
87883	0.0237	['Harry Styles']	0.676	207133	0.771	1M4qEo4HE3PRaCOM7EXNJq	0.000007	8	0.102	-3.675	1	Adore You	86	2019-12-06	0.0483	99.048	0.569	2019	[Harry Styles]	[]	[Harry Styles]	Harry StylesAdore You

	acousticness	artists	danceability	duration_ms	energy	explicit	id	instrumentalness	key	liveness	loudness	mode	name	popularity	release_date	speechiness	tempo	valence	year	artists_upd_v1	artists_upd_v2	artists_upd	artists_song
97046	0.1110	['Miley Cyrus']	0.583	278747	0.655	0	5AnCLGg35ziFOloEnXK4uu	0.000004	0	0.113	-5.407	1	Adore You	70	2013-10-04	0.0315	119.759	0.201	2013	[Miley Cyrus]	[]	[Miley Cyrus]	Miley CyrusAdore You
87868	0.0237	['Harry Styles']	0.676	207133	0.771	0	3jjujdWJ72nww5eGnfs2E7	0.000007	8	0.102	-3.675	1	Adore You	88	2019-12-13	0.0483	99.048	0.569	2019	[Harry Styles]	[]	[Harry Styles]	Harry StylesAdore You

	artists_upd	id	artists	acousticness	danceability	duration_ms	energy	instrumentalness	liveness	loudness	speechiness	tempo	valence	popularity	key	mode	count	genres	genres_upd
51108	Robert Schumann	6KuQTIu1KoTTkLXKrwlLPV	Robert Schumann	0.98417	0.362023	212320.169960	0.105301	0.782029	0.160324	-22.831075	0.048055	98.447067	0.277442	3.723320	5.0	1.0	253.0	['classical', 'early romantic era']	[classical, early_romantic_era]
51109	Vladimir Horowitz	6KuQTIu1KoTTkLXKrwlLPV	Vladimir Horowitz	0.99007	0.343210	266541.125104	0.118844	0.879508	0.183812	-23.193418	0.043360	94.900679	0.225951	3.592378	1.0	1.0	1207.0	['classical', 'classical performance', 'classi...	[classical, classical_performance, classical_p...

	id	followers	genres	name
0	0DheY5irMjBUeLybbCUEZ2	0.0	[]	Armid & Amir Zare Pashai feat. Sara Rouzbehani
1	0DlhY15l3wsrnlfGio2bjU	5.0	[]	ปูนา ภาวิณี
2	0DmRESX2JknGPQyO15yxg7	0.0	[]	Sadaa
3	0DmhnbHjm1qw6NCYPeZNgJ	0.0	[]	Tra'gruda
4	0Dn11fWM7vHQ3rinvWEl4E	2.0	[]	Ioannis Panoutsopoulos

	id	genres_upd	consolidates_genre_lists
0	000G1xMMuwxNHmwVsBdtj1	[[candy_pop, dance_rock, new_romantic, new_wav...	[new_romantic, candy_pop, power_pop, permanent...
1	000ZxLGm7jDlWCHtcXSeBe	[[boogie-woogie, piano_blues, ragtime, stride]]	[piano_blues, ragtime, stride, boogie-woogie]
2	000jBcNljWTnyjB4YO7ojf	[[]]	[]
3	000mGrJNc2GAgQdMESdgEc	[[classical, late_romantic_era], [historic_orc...	[historic_orchestral_performance, classical, o...
4	000u1dTg7y1XCDXi80hbBX	[[country, country_road, country_rock]]	[country_road, country_rock, country]

	acousticness	artists	danceability	duration_ms	energy	explicit	id	instrumentalness	key	liveness	loudness	mode	name	popularity	release_date	speechiness	tempo	valence	year	artists_upd_v1	artists_upd_v2	artists_upd	artists_song	consolidates_genre_lists
155464	0.0287	["Livin' Joy", 'A. Manetta']	0.704	215227	0.722	0	5cIU5GZBbyMfgfXGcoQVYc	0.00341	2	0.297	-10.815	0	Don't Stop Movin' - Radio Mix	56	1997-01-01	0.0446	129.992	0.859	1997	[ Joy", ]	[Livin' Joy]	[ Joy", ]	Joy", Don't Stop Movin' - Radio Mix	NaN
155465	0.0218	["Lil' Flip", 'Lea']	0.845	225187	0.346	0	3FaUH7ZMjW1hv9Jx6MIAIf	0.00000	0	0.135	-9.381	1	Sunshine (feat. Lea)	47	2004-03-30	0.1060	93.989	0.819	2004	[ Flip", ]	[Lil' Flip]	[ Flip", ]	Flip", Sunshine (feat. Lea)	NaN
155466	0.0516	["Ol' Dirty Bastard", 'Kelis', 'Rich Travali']	0.934	239547	0.459	1	6YYd5MLpu45J0uLrMdivF7	0.00000	1	0.222	-7.654	1	Got Your Money (feat. Kelis)	66	1999	0.1890	103.040	0.697	1999	[ Dirty Bastard", , , ]	[Ol' Dirty Bastard]	[ Dirty Bastard", , , ]	Dirty Bastard", Got Your Money (feat. Kelis)	NaN
155467	0.0249	["World Class Wreckin' Cru", "Michel 'Le"]	0.715	351040	0.490	0	3hoiinUc5VA9xUEJID7R8V	0.00017	9	0.139	-9.504	0	Turn Off The Lights - Rap	35	1994-04-06	0.0479	129.309	0.429	1994	[ Cru", "Michel ]	[World Class Wreckin' Cru, Michel 'Le]	[ Cru", "Michel ]	Cru", "Michel Turn Off The Lights - Rap	NaN
155468	0.0113	["Rappin' 4-Tay", 'MC Breed', 'Too $hort']	0.897	337973	0.414	1	78859Af0fmA9VTlgnOHTAP	0.00011	4	0.101	-8.450	0	Never Talk Down	35	1996	0.2460	96.039	0.273	1996	[ 4-Tay", , , ]	[Rappin' 4-Tay]	[ 4-Tay", , , ]	4-Tay", Never Talk Down	NaN

	acousticness	artists	danceability	duration_ms	energy	id	instrumentalness	key	liveness	loudness	mode	name	popularity	release_date	speechiness	tempo	valence	year	artists_upd_v1	artists_upd_v2	artists_upd	artists_song	consolidates_genre_lists	popularity_red
0	0.381	['黑豹']	0.353	316160	0.6860	3KIuCzckjdeeVuswPo20mC	0.000000	11	0.0568	-9.103	1	DON'T BREAK MY HEART	34	1992-12-22	0.0395	200.341	0.3520	1992	[黑豹]	[]	[黑豹]	黑豹DON'T BREAK MY HEART	[chinese_indie, chinese_indie_rock]	6
1	0.978	['黃蜀娟']	0.153	156813	0.0581	7dkMnOK1h9I3D2NyDDjBfh	0.000939	10	0.1550	-23.697	1	輕煙濃霧	23	1968-02-28	0.0472	174.060	0.0904	1968	[黃蜀娟]	[]	[黃蜀娟]	黃蜀娟輕煙濃霧	[]	4
2	0.828	['黃蜀娟']	0.508	100133	0.2470	7ilXPF7IilGtVX4MMubQfQ	0.000000	3	0.2970	-14.241	1	踏雪尋梅	23	1968-02-28	0.1730	170.629	0.6800	1968	[黃蜀娟]	[]	[黃蜀娟]	黃蜀娟踏雪尋梅	[]	4
3	0.909	['黃蜀娟']	0.140	284040	0.1310	3enJcaHwRwN3ABPp3Bae35	0.000042	10	0.6190	-20.946	1	藍色的月光	23	1968-01-28	0.0455	82.543	0.1160	1968	[黃蜀娟]	[]	[黃蜀娟]	黃蜀娟藍色的月光	[]	4
4	0.944	['黃蜀娟']	0.604	173627	0.2100	6aewX7cGeXnZTK36bZRI6T	0.000007	0	0.2110	-17.312	1	薔薇之戀	23	1968-01-28	0.0395	105.531	0.5380	1968	[黃蜀娟]	[]	[黃蜀娟]	黃蜀娟薔薇之戀	[]	4

	artist	name	id	url	date_added
39	Martin Garrix	Drown (feat. Clinton Kane)	4RVtBlHFKj51Ipvpfv5ER4	https://i.scdn.co/image/ab67616d00001e02b154bc...	2020-08-01 01:27:34+00:00
37	Riton	Turn Me On (feat. Vula)	0qaWEvPkts34WF68r8Dzx9	https://i.scdn.co/image/ab67616d00001e02216a27...	2020-07-09 01:34:03+00:00
34	RL Grime	UCLA	3OaunNUlXXs5e2PXtNAzzG	https://i.scdn.co/image/ab67616d00001e02eded2e...	2020-06-20 00:34:44+00:00
33	SAINt JHN	Roses - Imanbek Remix	7fPuWrlpwDcHm5aHCH5D9t	https://i.scdn.co/image/ab67616d00001e022b6e2f...	2020-04-19 06:26:21+00:00
30	Loud Luxury	Body	21RzyxY3EFaxVy6K4RqaU9	https://i.scdn.co/image/ab67616d00001e02af5e18...	2020-03-26 22:28:23+00:00
28	ZHU	Working For It	2HJQcyUpmUuvzS5vBAICIc	https://i.scdn.co/image/ab67616d00001e02bfaac9...	2019-12-19 15:53:47+00:00
26	Lastlings	Deja Vu	649HM5lOHHqsoG5nldMo6L	https://i.scdn.co/image/ab67616d00001e02129817...	2019-11-19 16:04:48+00:00
25	Avicii	Waiting For Love	2P4OICZRVAQcYAV2JReRfj	https://i.scdn.co/image/ab67616d00001e025393c5...	2019-11-17 03:38:47+00:00
24	Regard	Ride It	2tnVG71enUj33Ic2nFN6kZ	https://i.scdn.co/image/ab67616d00001e025c2781...	2019-11-13 04:13:21+00:00
20	Dimitri Vegas & Like Mike	Mammoth	76fqWMe0buqQoaNTIbLWmr	https://i.scdn.co/image/ab67616d00001e0216bf35...	2019-10-26 19:11:43+00:00
18	Sebastian Ingrosso	Reload - Radio Edit	5jyUBKpmaH670zrXrE0wmO	https://i.scdn.co/image/ab67616d00001e0270e2e5...	2019-10-04 15:50:31+00:00
22	Kygo	This Town (feat. Sasha Sloan)	4aSfgWmRa9KsISD4Jmx7QB	https://i.scdn.co/image/ab67616d00001e02a33355...	2019-09-30 20:05:19+00:00
14	Hayden James	Just Friends	6tB4XVKceo2307SSWXaO0y	https://i.scdn.co/image/ab67616d00001e024b6940...	2019-09-30 20:04:53+00:00
13	MEDUZA	Piece Of Your Heart	1DFD5Fotzgn6yYXkYsKiGs	https://i.scdn.co/image/ab67616d00001e02ead130...	2019-09-30 20:04:47+00:00
11	Dimitri Vegas & Like Mike	Tremor - Sensation 2014 Anthem	6AE0G24YXnDyEgE4L0efpB	https://i.scdn.co/image/ab67616d00001e023d4c4f...	2019-09-30 20:04:43+00:00
7	Tiësto	Secrets	0NIC4unbe5KZOp1d9T7OaF	https://i.scdn.co/image/ab67616d00001e02de5f51...	2019-09-30 20:04:40+00:00

	acousticness	artists	danceability	duration_ms	energy	explicit	id	instrumentalness	key	liveness	loudness	mode	name	popularity	release_date	speechiness	tempo	valence	year	artists_upd_v1	artists_upd_v2	artists_upd	artists_song	consolidates_genre_lists	popularity_red	sim	url
10730	0.06430	['Valerie Broussard', 'Galantis']	0.683	184564	0.785	0	23FHa9lYnG6Dr8OzombPkS	0.000013	7	0.1770	-4.879	1	Roots	69	2019-08-16	0.0370	122.997	0.5810	2019	[Valerie Broussard, Galantis]	[]	[Valerie Broussard, Galantis]	Valerie BroussardRoots	[tropical_house, edm, dance_pop, big_room, pop...	13	0.777350	https://i.scdn.co/image/ab67616d00001e02bfe4e6...
135509	0.01600	['Calvin Harris', "Rag'n'Bone Man"]	0.807	229184	0.887	0	5itOtNx0WxtJmi1TQ3RuRd	0.000503	1	0.0811	-4.311	0	Giant (with Rag'n'Bone Man)	80	2019-01-11	0.0361	122.015	0.6060	2019	[Calvin Harris, n]	[Rag'n'Bone Man]	[Calvin Harris, n]	Calvin HarrisGiant (with Rag'n'Bone Man)	[progressive_house, tropical_house, edm, uk_da...	16	0.744758	https://i.scdn.co/image/ab67616d00001e02a9a9d8...
70513	0.08100	['Loud Luxury', 'Bryce Vine']	0.875	187797	0.858	0	7fcEMgPlojD0LzPHwMsoic	0.000001	4	0.3810	-3.886	1	I'm Not Alright	65	2019-12-20	0.0496	121.978	0.7000	2019	[Loud Luxury, Bryce Vine]	[]	[Loud Luxury, Bryce Vine]	Loud LuxuryI'm Not Alright	[tropical_house, edm, pop_rap, dance_pop, pop,...	13	0.730603	https://i.scdn.co/image/ab67616d00001e02d48c7e...
106357	0.02820	['Galantis']	0.674	191293	0.915	0	6M6Tk58pQvABy6ru66dY3d	0.003370	6	0.2730	-3.999	0	No Money	69	2017-09-15	0.0411	126.038	0.7800	2017	[Galantis]	[]	[Galantis]	GalantisNo Money	[tropical_house, edm, dance_pop, big_room, pop...	13	0.728139	https://i.scdn.co/image/ab67616d00001e0271340c...
106353	0.11700	['Galantis', 'Throttle']	0.762	190400	0.797	0	5kgqTe1BM720OjU78TGYDw	0.000000	5	0.2020	-2.710	1	Tell Me You Love Me	60	2017-09-15	0.1350	122.066	0.5250	2017	[Galantis, Throttle]	[]	[Galantis, Throttle]	GalantisTell Me You Love Me	[tropical_house, edm, dance_pop, big_room, pop...	12	0.726095	https://i.scdn.co/image/ab67616d00001e0271340c...
100074	0.22900	['Gryffin', 'Katie Pearlman']	0.590	231291	0.764	0	17ejRbr6B8l9zdqgCZsn4m	0.000000	2	0.1920	-4.735	1	Nobody Compares To You (feat. Katie Pearlman)	66	2019-10-24	0.0467	104.911	0.3310	2019	[Gryffin, Katie Pearlman]	[]	[Gryffin, Katie Pearlman]	GryffinNobody Compares To You (feat. Katie Pea...	[tropical_house, edm, dance_pop, pop, electro_...	13	0.720858	https://i.scdn.co/image/ab67616d00001e020e5311...
106355	0.00104	['Galantis']	0.708	203133	0.945	0	3aIhJDHxr1kgTSnutJxPTH	0.080300	5	0.2270	-3.247	0	Peanut Butter Jelly	66	2015-06-05	0.2340	127.960	0.5450	2015	[Galantis]	[]	[Galantis]	GalantisPeanut Butter Jelly	[tropical_house, edm, dance_pop, big_room, pop...	13	0.719479	https://i.scdn.co/image/ab67616d00001e022b5179...
146679	0.08660	['Axwell /\\ Ingrosso', 'Axwell', 'Sebastian I...	0.467	254653	0.757	0	4b2tcjrG1qUkSdsqEFP2dB	0.000000	2	0.0742	-3.010	1	Sun Is Shining	72	2017-07-28	0.0517	131.993	0.3830	2017	[Axwell /\\ Ingrosso, Axwell, Sebastian Ingrosso]	[]	[Axwell /\\ Ingrosso, Axwell, Sebastian Ingrosso]	Axwell /\\ IngrossoSun Is Shining	[progressive_house, tropical_house, edm, dance...	14	0.717821	https://i.scdn.co/image/ab67616d00001e02fba6de...
106361	0.00463	['Galantis', 'OneRepublic']	0.658	205793	0.804	0	1pfgsjmxVZhoZpeDx6POKv	0.000000	0	0.1780	-5.735	1	Bones (feat. OneRepublic)	73	2019-01-31	0.0377	120.047	0.5080	2019	[Galantis, OneRepublic]	[]	[Galantis, OneRepublic]	GalantisBones (feat. OneRepublic)	[piano_rock, tropical_house, pop_rock, edm, da...	14	0.711158	https://i.scdn.co/image/ab67616d00001e02344d2f...
106354	0.00711	['Galantis']	0.506	227074	0.805	0	46lFttIf5hnUZMGvjK0Wxo	0.001930	1	0.0856	-4.119	1	Runaway (U & I)	75	2015-06-05	0.0469	126.008	0.3830	2015	[Galantis]	[]	[Galantis]	GalantisRunaway (U & I)	[tropical_house, edm, dance_pop, big_room, pop...	15	0.711128	https://i.scdn.co/image/ab67616d00001e022b5179...
38318	0.03350	['Sam Feldt', 'RANI', 'GATTÜSO']	0.542	207619	0.903	0	6b1RNvAcJjQH73eZO4BLAB	0.000005	4	0.1110	-2.419	0	Post Malone (feat. RANI) - GATTÜSO Remix	72	2019-08-29	0.0434	127.936	0.3670	2019	[Sam Feldt, RANI, GATTÜSO]	[]	[Sam Feldt, RANI, GATTÜSO]	Sam FeldtPost Malone (feat. RANI) - GATTÜSO Remix	[edm, dance_pop, big_room, uk_pop, pop, pop_ed...	14	0.705535	https://i.scdn.co/image/ab67616d00001e028dedf4...
151700	0.26900	['Alesso']	0.601	190295	0.775	0	6jreFSOTUAViWjKyzOC4Kg	0.000000	0	0.0883	-4.612	1	REMEDY	74	2018-08-31	0.0473	119.964	0.4830	2018	[Alesso]	[]	[Alesso]	AlessoREMEDY	[tropical_house, edm, dance_pop, big_room, pro...	14	0.705345	https://i.scdn.co/image/ab67616d00001e02a74798...
122874	0.00292	['Dirty South', 'Alesso', 'Ruben Haze']	0.478	226867	0.818	0	54ZPmGE1uOG9IYoUBSRSp7	0.007890	4	0.4380	-5.076	1	City Of Dreams - Radio Edit	60	2013-01-01	0.0377	127.953	0.1630	2013	[Dirty South, Alesso, Ruben Haze]	[]	[Dirty South, Alesso, Ruben Haze]	Dirty SouthCity Of Dreams - Radio Edit	[progressive_house, tropical_house, edm, dance...	12	0.705294	https://i.scdn.co/image/ab67616d00001e02b60a6d...
151703	0.03650	['Alesso', 'Roy English']	0.537	221400	0.848	0	2ToIksTPpJ4csKPEOdUEyM	0.000000	6	0.4230	-2.431	0	Cool	59	2015-05-26	0.0313	128.023	0.4980	2015	[Alesso, Roy English]	[]	[Alesso, Roy English]	AlessoCool	[tropical_house, edm, dance_pop, big_room, pro...	11	0.696263	https://i.scdn.co/image/ab67616d00001e02b804bb...
31186	0.02460	['Steve Aoki', 'Moxie']	0.639	191653	0.795	0	6vscP7Fweq7fwosyliwjRq	0.000324	9	0.5090	-3.535	1	I Love It When You Cry (Moxoki) - Radio Edit	54	2015-05-11	0.0416	128.013	0.4540	2015	[Steve Aoki, Moxie]	[]	[Steve Aoki, Moxie]	Steve AokiI Love It When You Cry (Moxoki) - Ra...	[edm, dance_pop, big_room, tropical_house, ele...	10	0.696229	https://i.scdn.co/image/ab67616d00001e02838fa1...
106352	0.00837	['Galantis']	0.184	221286	0.871	0	7gUg4GdOA6Y6yRDe36GIjK	0.167000	8	0.1500	-3.245	0	You	53	2015-06-05	0.0661	127.972	0.2100	2015	[Galantis]	[]	[Galantis]	GalantisYou	[tropical_house, edm, dance_pop, big_room, pop...	10	0.695491	https://i.scdn.co/image/ab67616d00001e022b5179...
65200	0.45800	['Martin Garrix', 'Dean Lewis']	0.651	236765	0.693	0	7pWK1kMgHy5lNNiIfuRbkP	0.000000	4	0.3350	-4.722	1	Used To Love (with Dean Lewis)	76	2019-10-31	0.0375	118.970	0.3920	2019	[Martin Garrix, Dean Lewis]	[]	[Martin Garrix, Dean Lewis]	Martin GarrixUsed To Love (with Dean Lewis)	[progressive_house, tropical_house, edm, big_r...	15	0.695212	https://i.scdn.co/image/ab67616d00001e02dcc283...
65216	0.00107	['Martin Garrix']	0.675	304229	0.868	1	1TWfkGrhF7ob0nwB2M6knb	0.715000	1	0.3740	-6.360	1	Animals	66	2013-07-01	0.0392	128.007	0.0376	2013	[Martin Garrix]	[]	[Martin Garrix]	Martin GarrixAnimals	[progressive_house, tropical_house, edm, big_r...	13	0.688356	https://i.scdn.co/image/ab67616d00001e02eb6f61...
65215	0.00165	['Martin Garrix']	0.677	303827	0.866	0	6JEntXLt4z98CcDtIH9sU7	0.662000	1	0.4120	-6.403	1	Animals - Extended	52	2013-01-01	0.0392	128.005	0.0386	2013	[Martin Garrix]	[]	[Martin Garrix]	Martin GarrixAnimals - Extended	[progressive_house, tropical_house, edm, big_r...	10	0.687930	https://i.scdn.co/image/ab67616d00001e029564b7...
31185	0.00377	['Steve Aoki', 'Louis Tomlinson']	0.647	198774	0.932	0	508oFmt92FyICj6pZiWQwC	0.000002	11	0.0574	-3.517	1	Just Hold On	60	2018-11-09	0.0830	115.000	0.3870	2018	[Steve Aoki, Louis Tomlinson]	[]	[Steve Aoki, Louis Tomlinson]	Steve AokiJust Hold On	[edm, dance_pop, big_room, pop, tropical_house...	12	0.686015	https://i.scdn.co/image/ab67616d00001e02c37b50...
151699	0.00234	['Alesso', 'Matthew Koma']	0.368	195467	0.823	0	5pVk15sR3OgIeKBKqG9jWw	0.000002	2	0.1740	-6.245	1	Years	48	2013-01-01	0.0804	128.004	0.3230	2013	[Alesso, Matthew Koma]	[]	[Alesso, Matthew Koma]	AlessoYears	[tropical_house, edm, dance_pop, big_room, pro...	9	0.685903	https://i.scdn.co/image/ab67616d00001e0252c261...
151701	0.08420	['Alesso', 'Nico & Vinz']	0.655	240413	0.818	0	2kYqdSlrtovVMMIn6ykzba	0.000000	2	0.1060	-3.626	1	I Wanna Know	63	2016-04-01	0.0330	115.971	0.4740	2016	[Alesso, Nico & Vinz]	[]	[Alesso, Nico & Vinz]	AlessoI Wanna Know	[tropical_house, edm, pop_rap, dance_pop, big_...	12	0.682655	https://i.scdn.co/image/ab67616d00001e02a12c77...
106365	0.58700	['Gala']	0.704	213394	0.861	0	3u5N55tHf7hXATSQrjBh2q	0.061500	2	0.0992	-4.221	0	Freed From Desire	72	2007-04-18	0.0493	128.990	0.6360	2007	[Gala]	[]	[Gala]	GalaFreed From Desire	[tropical_house, edm, dance_pop, big_room, pop...	14	0.682282	https://i.scdn.co/image/ab67616d00001e02e3a264...
38863	0.49500	['SZA', 'Calvin Harris', 'Funk Wav']	0.775	171806	0.573	0	0P6AWOA4LG1XOctzaVu5tt	0.000000	11	0.1260	-4.933	1	The Weekend - Funk Wav Remix	78	2017-12-15	0.0585	101.925	0.6670	2017	[SZA, Calvin Harris, Funk Wav]	[]	[SZA, Calvin Harris, Funk Wav]	SZAThe Weekend - Funk Wav Remix	[rap, progressive_house, tropical_house, edm, ...	15	0.680499	https://i.scdn.co/image/ab67616d00001e02b19d1c...
146678	0.00509	['Axwell']	0.634	426947	0.846	0	2EjN9jtgovwI8XSYnhoVmf	0.695000	11	0.2290	-7.848	0	Feel The Vibe	52	2005-07-16	0.0456	128.009	0.8180	2005	[Axwell]	[]	[Axwell]	AxwellFeel The Vibe	[tropical_house, edm, dance_pop, big_room, pop...	10	0.680430	https://i.scdn.co/image/ab67616d00001e02ea6dc3...
37000	0.00864	['Sebastian Ingrosso', 'Alesso', 'Ryan Tedder']	0.416	205440	0.849	0	5Sey3HgGa6KB46mlOyCClZ	0.000138	1	0.2040	-4.909	0	Calling (Lose My Mind) - Radio Edit	56	2012-01-01	0.0455	125.098	0.1470	2012	[Sebastian Ingrosso, Alesso, Ryan Tedder]	[]	[Sebastian Ingrosso, Alesso, Ryan Tedder]	Sebastian IngrossoCalling (Lose My Mind) - Rad...	[progressive_house, tropical_house, edm, dance...	11	0.675258	https://i.scdn.co/image/ab67616d00001e02f9b356...
65211	0.05000	['Martin Garrix']	0.534	230635	0.711	0	6EsH66Uto1zwZlDGQ6RokU	0.003100	10	0.1350	-5.927	0	Forbidden Voices	64	2015-02-23	0.0368	128.102	0.3000	2015	[Martin Garrix]	[]	[Martin Garrix]	Martin GarrixForbidden Voices	[progressive_house, tropical_house, edm, big_r...	12	0.675015	https://i.scdn.co/image/ab67616d00001e025a68d0...
54230	0.02590	['Oliver Heldens', 'Boy Matthews']	0.751	156913	0.714	0	5fYN8kPYyAWWJUBX57vmXb	0.000504	2	0.0508	-6.502	0	Details (feat. Boy Matthews)	70	2020-04-17	0.0379	120.007	0.5290	2020	[Oliver Heldens, Boy Matthews]	[]	[Oliver Heldens, Boy Matthews]	Oliver HeldensDetails (feat. Boy Matthews)	[progressive_house, edm, future_house, uk_danc...	14	0.671667	https://i.scdn.co/image/ab67616d00001e0204ba16...
135503	0.09350	['Calvin Harris']	0.819	219160	0.913	0	1vvNmPOiUuyCbgWmtc6yfm	0.000037	4	0.1610	-3.059	0	My Way	76	2016-09-16	0.0427	119.989	0.5360	2016	[Calvin Harris]	[]	[Calvin Harris]	Calvin HarrisMy Way	[progressive_house, tropical_house, edm, uk_da...	15	0.670564	https://i.scdn.co/image/ab67616d00001e028d12bc...
59994	0.17000	['Moguai', 'Cheat Codes']	0.622	144910	0.802	0	2FGh2ref26EwA3Z5st5oHb	0.000000	9	0.2630	-4.969	0	Hold On (feat. Cheat Codes) - 2020 Edit	68	2020-03-06	0.1670	124.859	0.7640	2020	[Moguai, Cheat Codes]	[]	[Moguai, Cheat Codes]	MoguaiHold On (feat. Cheat Codes) - 2020 Edit	[german_techno, progressive_house, tropical_ho...	13	0.669640	https://i.scdn.co/image/ab67616d00001e024277f1...
146759	0.07150	['Avicii']	0.592	277262	0.873	0	0vrmHPfoBadXVr2n0m1aqZ	0.009820	9	0.1420	-5.650	1	Heaven	76	2019-06-06	0.0275	122.011	0.5160	2019	[Avicii]	[]	[Avicii]	AviciiHeaven	[pop, dance_pop, big_room, edm]	15	0.668757	https://i.scdn.co/image/ab67616d00001e02660ee2...
78109	0.16600	['Kygo', 'Valerie Broussard']	0.673	208567	0.596	0	3Kuu5vASpXK8oRsxOvau6P	0.000000	10	0.1100	-7.891	1	Think About You	68	2019-02-14	0.0354	123.969	0.1900	2019	[Kygo, Valerie Broussard]	[]	[Kygo, Valerie Broussard]	KygoThink About You	[pop, tropical_house, edm]	13	0.668087	https://i.scdn.co/image/ab67616d00001e02a65683...
135502	0.03700	['Calvin Harris', 'Dua Lipa']	0.791	214847	0.862	0	7ef4DlsgrMEH11cDZd32M6	0.000022	9	0.0814	-3.240	0	One Kiss (with Dua Lipa)	84	2018-04-06	0.1100	123.994	0.5920	2018	[Calvin Harris, Dua Lipa]	[]	[Calvin Harris, Dua Lipa]	Calvin HarrisOne Kiss (with Dua Lipa)	[progressive_house, tropical_house, edm, uk_da...	16	0.663355	https://i.scdn.co/image/ab67616d00001e02d09f96...
146749	0.27200	['Avicii', 'Aloe Blacc']	0.802	157202	0.645	0	2x0RZdkZcD8QRI53XT4GI5	0.000000	5	0.1190	-6.181	0	SOS (feat. Aloe Blacc)	80	2019-06-06	0.0715	100.001	0.3760	2019	[Avicii, Aloe Blacc]	[]	[Avicii, Aloe Blacc]	AviciiSOS (feat. Aloe Blacc)	[edm, dance_pop, big_room, r&b, pop]	16	0.663076	https://i.scdn.co/image/ab67616d00001e02660ee2...
135495	0.02110	['Calvin Harris']	0.596	222533	0.856	0	6YUTL4dYpB9xZO5qExPf05	0.017800	4	0.1410	-3.556	0	Summer	81	2014-10-31	0.0346	127.949	0.7430	2014	[Calvin Harris]	[]	[Calvin Harris]	Calvin HarrisSummer	[progressive_house, tropical_house, edm, uk_da...	16	0.662278	https://i.scdn.co/image/ab67616d00001e02063c04...
59993	0.19400	['Moguai', 'Cheat Codes']	0.611	178560	0.785	0	0XnHIhm9ppEHHDSRESdEcV	0.000000	5	0.0956	-5.721	1	Hold On (feat. Cheat Codes) - Radio Edit	68	2015-09-11	0.0707	125.030	0.5450	2015	[Moguai, Cheat Codes]	[]	[Moguai, Cheat Codes]	MoguaiHold On (feat. Cheat Codes) - Radio Edit	[german_techno, progressive_house, tropical_ho...	13	0.661663	https://i.scdn.co/image/ab67616d00001e024e2c1a...
52870	0.00533	['Otto Knows']	0.679	357507	0.732	0	2nIDPOUOprhe14XoCK6gxw	0.117000	3	0.0880	-7.089	1	Million Voices	44	2012-01-01	0.0526	125.981	0.2680	2012	[Otto Knows]	[]	[Otto Knows]	Otto KnowsMillion Voices	[progressive_house, edm, dance_pop, big_room, ...	8	0.661482	https://i.scdn.co/image/ab67616d00001e02f594e0...
146289	0.01140	['BTS', 'Steve Aoki']	0.606	307676	0.848	0	75scDPqGs75FotglJSoOI2	0.000001	11	0.2060	-3.952	1	MIC Drop (Steve Aoki Remix) [Full Length Edition]	74	2018-08-24	0.1840	170.010	0.5400	2018	[BTS, Steve Aoki]	[]	[BTS, Steve Aoki]	BTSMIC Drop (Steve Aoki Remix) [Full Length Ed...	[k-pop_boy_group, edm, dance_pop, big_room, k-...	14	0.661367	https://i.scdn.co/image/ab67616d00001e026feb6d...
52869	0.00220	['Otto Knows']	0.582	192867	0.894	0	0MOiv7WTXCqvm89lVCf9C8	0.022300	8	0.0664	-6.298	1	Million Voices - Radio Edit	66	2012-01-01	0.0410	125.946	0.0694	2012	[Otto Knows]	[]	[Otto Knows]	Otto KnowsMillion Voices - Radio Edit	[progressive_house, edm, dance_pop, big_room, ...	13	0.661191	https://i.scdn.co/image/ab67616d00001e026368be...
65214	0.00137	['Martin Garrix']	0.593	176120	0.914	0	65G7XDGcybJiGywSCXJiL5	0.445000	1	0.0714	-5.351	1	Animals - Radio Edit	58	2016-12-16	0.0363	128.015	0.0381	2016	[Martin Garrix]	[]	[Martin Garrix]	Martin GarrixAnimals - Radio Edit	[progressive_house, tropical_house, edm, big_r...	11	0.659130	https://i.scdn.co/image/ab67616d00001e02a8bbff...